Package

r-bayesm

Description

Bayesian Inference for Marketing/Micro-Econometrics. Covers many
important models used in marketing and micro-econometrics applications.
The package includes: Bayes Regression (univariate or multivariate dep
var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal
Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit, Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering), Dirichlet
Process Prior Density Estimation with normal base, Hierarchical Linear
Models with normal prior and covariates, Hierarchical Linear Models with
a mixture of normals prior and covariates, Hierarchical Multinomial
Logits with a mixture of normals prior and covariates, Hierarchical
Multinomial Logits with a Dirichlet Process prior and covariates,
Hierarchical Negative Binomial Regression Models, Bayesian analysis of
choice-based conjoint data, Bayesian treatment of linear instrumental
variables models, Analysis of Multivariate Ordinal survey data with
scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian
Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
Jiang, Manchanda, Rossi 2009) For further reference, consult our book,
Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley
2005) and Bayesian Non- and Semi-Parametric Methods and Applications
(Princeton U Press 2014).

Note